code: def tokenize(self, tokenizer):
      def tokenize_span(
          tokenizer, sentence: str, char_span: ExclusiveSpan
      ):
          """Tokenizes sentence and projects char_span to token span.
  
          Args:
              tokenizer (transformers.PreTrainedTokenizer): Tokenizer used
              sentence (str): Sentence to be tokenized
              char_span (ExclusiveSpan): character indexed span for sentence
  
          Returns:
              sentence_target_tokenization (List[str]): tokenized sentence
              target_span (ExclusiveSpane): token span for sentence
          """
          span_start_idx = len(sentence[: char_span.start].split())
          # If the first word in a span starts with punctuation, the first word will
          # erroneously be split into two strings by .split().
          # ie: 'takeaway' -> ["'", "takeaway"]
          # For span alignment, we start the list index at the punctuation.
          if (span_start_idx != 0) and (
              sentence[: (char_span.start)][-1] in string.punctuation
          ):
              span_start_idx = span_start_idx - 1
          span_text = sentence[char_span.start : char_span.end]
  
          sentence_space_tokenization = sentence.split()
          sentence_target_tokenization = tokenizer.tokenize(sentence)
          (
              sentence_normed_space_tokenization,
              sentence_normed_target_tokenization,
          ) = normalize_tokenizations(
              sentence_space_tokenization,
              sentence_target_tokenization,
              tokenizer,
          )
          span_start_char = len(
              " ".join(
                  sentence_normed_space_tokenization[:span_start_idx]
              )
          )
          span_text_char = len(span_text)
          aligner = retokenize.TokenAligner(
              sentence_normed_space_tokenization,
              sentence_normed_target_tokenization,
          )
          target_span = ExclusiveSpan(
              *aligner.project_char_to_token_span(
                  span_start_char, span_start_char + span_text_char
              )
          )
          return sentence_target_tokenization, target_span
  
      sentence1_target_tokenization, target_span1 = tokenize_span(
          tokenizer, self.sentence1, self.span1
      )
      sentence2_target_tokenization, target_span2 = tokenize_span(
          tokenizer, self.sentence2, self.span2
      )
  
      return TokenizedExample(
          guid=self.guid,
          sentence1_tokens=sentence1_target_tokenization,
          sentence2_tokens=sentence2_target_tokenization,
          word=tokenizer.tokenize(
              self.word
          ),  # might be more than one token
          sentence1_span=target_span1,
          sentence2_span=target_span2,
          label_id=WiCTask.LABEL_TO_ID[self.label],
      )
  
imports: import numpy
  import torch
  from dataclasses import dataclass
  from typing import List
  import string
  from jiant.tasks.core import BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, SuperGlueMixin, Task, TaskTypes
  from jiant.tasks.lib.templates.shared import labels_to_bimap, add_cls_token, create_input_set_from_tokens_and_segments
  from jiant.tasks.utils import truncate_sequences, ExclusiveSpan
  from jiant.utils.python.io import read_json_lines
  from jiant.utils import retokenize
  from jiant.utils.tokenization_normalization import normalize_tokenizations
label_desc: Use image node_id 1 for calling the Example obj's underlying member method code with example usage: obj.tokenize(tokenizer) and returns: TokenizedExample, sentence_target_tokenization,  target_span